Distance learning and the faculty: an analysis of perceptions, concerns, and opportunities.

Gerlich, Nick ; Wilson, Pamela H.

ABSTRACT

Higher education is experiencing a major paradigm shift from the traditional lecture/face-to-face learning environment to online/distance learning. Studies showing student, faculty, administrative, and the institution's adjustments, concerns and attitudes are now becoming available. However, this information is changing very rapidly, as the implementation of new distance learning delivery modes and methods become available. This study focuses on the difference in attitudes and concerns of faculty determined by their age, gender, tenure, PC literacy and whether they have taught an online class previously. Results indicated that the greatest disparity in faculty perceptions of online teaching were apparent between those with and without online teaching experience. Other factors, such as age, gender, tenure, and computer literacy, played little or no role in perceptual differences.

INTRODUCTION

Online education has grown and prospered in the ten years following the commercial development of the internet. Private and public universities, as well as private firms, have embraced the concept in large numbers, as have students. Still, there are concerns about the new paradigm, often centered on faculty perceptions.

These perceptions often include concerns about the quality of teaching, the amount of preparation, the level of student-faculty interaction, and technical support provided by the university (Schiffer 2002; Meyer 2002; Bower 2001; Crumpacker 2001). These concerns are typical of schools with little or no prior experience in online learning, and thus may not reflect views after experience is gained.

The purpose of this study is to examine full-time faculty views on distance learning at an institution that has been delivering online courses and programs for over five years, and was one of the first movers among public universities in its home state.

LITERATURE REVIEW

This study focuses on concerns and barriers to effective online/distance learning from the faculty point of view. "Technological change is what many have said is the only constant in our work today" (Kubala, 2000). Development of distance education technologies requires that faculty adjust their teaching styles, course design, evaluation of student work and in essence, the way they think about education and educational tools available to them. Thus, a major a paradigm shift, from lecture/face-to-face classes to technologically advanced online/distance learning. (NEA, 2002; Quinn and Corry, 2002; Oblinger, Barone and Hawkins, 2001; Hassenplug and Harnish, 1998).

Have faculty made this paradigm shift? According to a survey conducted by the NEA, one in 10 higher education NEA members teaches a distance learning course and 90% of these NEA members who teach traditional courses say that distance learning courses are offered or being considered at their institutions. (NEA, 2000). As stated in this survey, "Distance learning NEA members resemble traditional faculty in that they are full time (80%), tenured (73%), split evenly between full professors (35) and lecturers and adjuncts (35%), hold masters' degrees (48%) rather than a Ph.D. (31%)" (NEA, 2000). From the above statistics, we can dispel the notion that traditional faculty are being replaced by part-time distance learning faculty, allowing for the fact that many distance learning faculty, teaching only one or two courses, would probably not be members of the NEA (NEA 2000).

If a large number of full-time and tenured faculty are teaching distance/online learning classes, then what are their attitudes and concerns? Only recently has literature been available to review to give further insight to these issues.

One recurring theme in recent literature is the issue of increased preparation time or workload increase when teaching distance/online classes. Several studies concluded that distance/online learning requires a disproportionate investment of time and effort for preparation than traditional face-to-face classes (Carnevale 2001; Schneider 2000; Carr 2000b; National Education Association 2000; American Association of University Professors 1999). Along with workload considerations, distance/online learning faculty are concerned about appropriate compensation for the work (Meyen and Yang 2003; Lynch and Corry 1998). However, regardless of preparation time, workload, or compensation issues the National Center for Education Statistics (2002) found that "... despite carrying larger teaching loads, faculty who taught any distance classes were just as likely, and in some cases more likely, to indicate that they were very satisfied with their workload, compared with faculty teaching only traditional classes." This was also found to be the case in a survey by the National Education Association (2000).

Some critics believe that distance/online learning is not a substitute for students interacting spontaneously in a face-to-face environment with other students and professors (Guernsey 1998; Sherron and Boettcher 1997; Black 1992). However, other studies show that there may be benefits and more options available in distance/online learning than are available in the face-to-face learning environment (Turoff 1999; Sherron and Boettcher 1997).

Another concern is that of tenured versus non-tenured faculty. Are tenured or non-tenured faculty more likely to make the paradigm shift to distance/online learning? The National Center for Education Statistics (2002) states that "the security of tenure might encourage experienced faculty to try more controversial forms of instructional design ..." This seems to be contrary to the fact that tenured faculty have more years of experience in teaching and might be less likely to want to change their methods of teaching.

Many studies show that distance/online teaching faculty are concerned about the level of student /faculty interaction when using distance technologies. Some disagree that the kind of interaction the distance education student experiences is comparable to the face-to-face teaching/learning environment (Gladieux and Swail 1999; Sherron and Boettcher 1997). However, the National Center for Education Statistics (2002) stated that "faculty who participated in distance education appeared to interact with students, or be available to them, more than their non distance counterparts in fall 1998. Full-time faculty teaching distance classes held slightly more office hours per week than their peers who did not teach distance education classes or non-face-to-face classes." Many distance educators perceive some of the greatest barriers to teaching in a distance environment as technology issues; either not having the needed technology, or not having the technological support to successfully implement distance/online classes. In addition, distance faculty are also concerned with the content and quality of their classes. (Meyen and Yang 2003; Greenagel 2002; Berge 1998). One survey revealed 43% of the respondents had concerns about "content" and 31% expressed concerns about "technical issues", such as not having the necessary equipment (DDI 2002) and another report by Killion (2000) reported faculty concerns about content and learning methods employed.

The initial costs, hidden costs and ongoing costs of distance/online learning environments can also be a detriment when developing distance/online learning environments (National Staff Development Council and National Institute for Community Innovations 2001; Killion 2000; One study identified 22 barriers to online staff development programs that ranged from lack of technology, limited time factors, limited budgets, not having the expertise to develop classes, lack of incentives for instructional faculty to participate and others (Meyen and Yang, 2003).

Although advantages and disadvantages of distance/online learning are still being studied, educators and researchers will have a plethora of research opportunities in the foreseeable future as the educational paradigm continues to shift towards distance/online learning and away from the traditional face-to-face teaching modes and methods.

METHODOLOGY

Data were collected at a medium-sized Division II public university in the Sun Belt. This university has been delivering online courses since 1997, starting with one course and 25 students, to its current level of over 75 courses and over 4300 course enrollments.

An email announcement was sent to all 226 full-time faculty, with a link to an online survey instrument. Of these, 110 submitted the survey (48.7%). Respondents remained anonymous, and constitute a volunteer sample, since all full-time faculty were invited to participate.

Exploratory research was conducted to determine the key issues surrounding online education deemed important by the faculty. A series of 14 Likert-type statements were developed and included in the survey, along with five faculty demographic variables that would be used for detailed analysis of the data.

The Likert statements included in the instrument are found in Table 1 below. The five demographic variables were (1) whether the faculty member had taught online, (2) gender, (3) PC literacy, (4) age group, and, (5) tenure status. Several open-ended questions were also provided, to which faculty members could elaborate on their primary concerns. Data were analyzed using SPSS-PC software.

The demographic variables were categorized as follows:

 Online experience: Yes or No
 Gender: Male or Female
 PC Literacy: High or Medium vs.
 Low or None Age: 40 and under vs. over-40
 Tenure: Yes or No

RESULTS

Mean responses for each of the 14 Likert statements were calculated, and then broken down by each of the five demographic variables (see Tables 2-6 for results). A mean response of 3 indicates overall neutrality to an issue, while an average score greater than 3 indicates an increasing level of disagreement, and an average score less than 3 indicates an increasing level of agreement. T-tests for independent samples were performed for each of these comparisons, and the probability of these differences occurring by chance.

Table 7 summarizes which mean scores were significantly different (at p<0.05) for each of the 14 statements and 5 demographic variables. Of the 70 possibilities, 17 analyses resulted in significant differences.

Perhaps the most important result is that, after five years of offering online courses and programs, the one demographic variable producing the most significant differences in responses is whether or not the faculty member had ever taught online. Of the 14 Likert statements, eight produced significantly different mean responses.

Results for the other demographic variables were not as compelling. Gender produced 5 of 14 significant differences, while PC literacy produced 3 and tenure 1. There were no significant differences for the age variable.

Table 3 summarizes the data between online and offline faculty. Specifically, the online faculty disagreed more with the statement that too many courses were offered, suggesting they think that more could be offered (Q#1). Online faculty also demonstrated a sizeable difference in their disagreement with the statement that online teaching is less effective than traditional formats (Q#3). Other responses echoed these findings, revealing that the online faculty contend there is great demand for more online courses (Q#7), that students learn as much in online courses as they do in other courses (Q#11), online students receive value for their money (Q#12), and that faculty with online experience prefer this method (Q#10).

Table 4 summarizes the data between male and female respondents. Five of the 14 items resulted in significant differences, indicating possibly that women are more inclined to favor online courses because of the clear advantages such courses offer female students (especially those who are married and/or with children).

For example, males were more likely than females to feel that online teaching is less effective than on-campus teaching (Q#3), yet men were also more likely to prefer to teach online than were women (Q#10). Women were more likely to feel that online students get value for their money (Q#12), and that student comments have been favorable (Q#4), while disagreeing strongly that there are too many online courses (Q#1).

The other demographic variables (PC literacy, age, and tenure status) did not produce many significant results, leading us to conclude that these factor were not relevant pivot points for the data. This is somewhat surprising, since online teaching assumes a certain level of PC literacy. Furthermore, age is often assumed to be a factor in PC literacy, since younger faculty have been exposed to computer technologies for a greater percentage of their lives than have their more senior colleagues.

Finally, tenure was not a good source of perceptual differences. Given the pressures of attaining tenure, one might conclude that previously-tenured faculty might be less favorably disposed toward a paradigm that would require them to learn new pedagogy and computing skills, at a point in their career when it might not be critical to do so.

CONCLUSIONS

The results reported above point to an interesting observation: After five years of delivering courses and programs online, the biggest factor producing differences of opinion is simply whether the faculty member had ever taught online. Generally speaking, experienced online faculty were more favorable in their assessments of this paradigm than were faculty with no online experience. While it is not possible to determine from this study if these online faculty were naturally predisposed to the paradigm (or the opposite for other faculty not teaching online), it may be possible to improve overall perceptions of online teaching by merely getting more offline faculty into the ranks of online faculty.

No attempt was made to analyze for differences among the experienced online faculty. It is possible that their assessments improve as their number of online experiences increases. Still, it is apparent from these results that by increasing from 0 to 1 or more the number of online teaching experiences, a generally more favorable outlook toward online teaching will result.

REFERENCES

American Association of University Professors, Special committee on distance education and intellectual property issues. (1999). Distance Education and Intellectual Property. Academe, 85(3): 41-45.

Black, E.J. (1992). Faculty support for university distance education. CADE: Journal of Distance Education, 7(2): 5-30.

Berge, Z. (1998). Barriers to online teaching in post-secondary institutions: Can policy changes fix it? Retrieved

February 03, 2004, from Online Journal of Distance Learning Administration, 1(2). http://www.westga.edu/distance/berge12.html

Bower, B. (2001). Distance education: Facing the faculty challenge. Retrieved February 20, 2004, from the Online Journal of Distance Learning Administration, 4(2) http://www.westga.edu/~distance/ojdla/summer42/summer42.html

Carnevale, D. (2001, February 23). As online education surges, some colleges remain untouched. Chronicle of Higher Education, 47(24): A41-A42

Carr, S. (2000b, July 7). Many professors are optimistic on distance learning, survey finds. Chronicle of Higher Education, 46(44): A35.

Crumpacker, N. (2001). Faculty pedagogical approach, skill, and motivation in today's distance education milieu. Retireved February 20, 2004, from the Online Journal of Distance Learning Administration 4(4). http://www.westga.edu/~distance/ojdla/winter44/crumpacker44.html

Development Dimensions International (DDI) (2002, August 23). In Pittsburgh, Aug. 23, 2002 (Business wire). Access to Global Knowledge Network. Retrieved February XX, 2004 from http://www.access.globalknowledge.com/article.asp?ID=4142

Gladieux, L. E. & Swail, W. S. (1999). The virtual university and educational opportunity: Issues of equity and access for the next generation. Washington, DC: The College Board.

Greenagel, F. L. (2002). The illusion of e-learning: Why we are missing out on the promise of technology. Retrieved February 03, 2004 from http://www.league.org/publication/whitepapers/0802.html

Guernsey, L. (1998, March 27). Colleges debate the wisdom of having on-campus students enroll in on-line classes. Chronicle of Higher Education, 44(290: A29.

Hassenplug, C. & Harnish, D. (1998). The nature and importance of interaction in distance education credit classes at a technical institute. Community College Journal of Research and Practice, (22), 591-605.

Killion, J. (2000). Log on to learn: To reap the benefits of online staff development, ask the right questions. Journal of Staff Development, 21(3), 48-53.

Kubala, T. (2000). Teaching community college faculty members on the internet. Community College Journal of Research and Practice, (24), 331-339.

Lynch, W. & Corry, M. (1998, March 10-14). Faculty recruitment, training, and compensation for distance education. Paper presented at the Society for Information Technology and Teacher Education, Washington, DC.

Meyen, E. L. & Yang, C.H. (2003). Barriers to implementing large-scale online staff development programs for teachers. Retrieved February 18, 2004, from Online Journal of Distance Learning Administration, 6(4). http://www.westga.edu/~distance/ojdla/winter64/meyen64.htm

Meyer, K. A. (2002). Does Policy make a difference? An exploration into policies for distance education. Retrieved February 20, 2004, from Online Journal of Distance Learning Administration 5(4). http://www.westga.edu/~distance/ojdla/winter54/Meyer_policy_54.htm

National Center for Education Statistics. (2002). Distance education instruction by postsecondary faculty and staff: Fall 1998. Retrieved February 19, 2004, from http://www.nces.ed.gov/pubsearch/pubsinfo.asp?pubid=200222155

National Education Association (2000). A survey of traditional and distance learning higher education members. Washington, DC: Author.

National Staff Development Council and National Institute for Community Innovations. (2001). E-learning for educators: Implementing the standards for staff development. Retrieved February XX, 2004 from http://www.nsdc.org/e-learning.pdf

Oblinger, D.G., Barone, C.A. & Hawkins, B.L. (2001). Distributed education and its challenges: An overview. Washington, DC: American Council on Education and EDUCAUSE.

O'Quinn, L. & Corry, M. (2002). Factors that deter faculty from participating in distance education. Retrieved February 20, 2004, from Online Journal of Distance Learning Administration, 5(4). http://www.westga.edu/~distance/ojdla/winter54/Quinn54.htm

Schiffer, C. (2002). Perception differences about participating in distance education. Retrieved February 20, 2004, from Online Journal of Distance Learning Administration 5(1). http://www.westga.edu/~distance/ojdla//spring51/schiffer51.html

Schneider, C. G. (2000, April 26). The age of virtual learning? Education Week, 19(33): 64.

Sherron, G.T. & Boettcher, J.V. (1997). Distance learning: The shift to interactivity, CAUSE Professional Paper Series 17. Boulder, CO: CAUSE.

Turoff, M. (1999, November). An end to student segregation: No more separation between distance learning and regular courses. Paper presented at the Summary of Invited Plenary for Telelearning 99, Montreal, Canada.

Nick Gerlich, West Texas A&M University

Pamela H. Wilson, West Texas A&M University

Table 1: Survey Instrument

Respondents were given 14 Likert-type statements and asked to rate
their level of agreement or disagreement with the statement. A
score of 1 indicated "strongly agree" while a score of 5 indicated
"strongly disagree." A score of 3 indicated neutrality while 2
was "agree" and 4 was "disagree."

Q1: The university's online program offers too many courses.

Q2: The University provides its online faculty with sufficient
 computer and staff resources to be able to teach online
 effectively.

Q3: Online teaching is less effective than teaching using the
 regular on-campus format.

Q4: Most student comments about courses they have taken through
 the online program have been favorable.

Q5: Many students believe the Online program offers too few course
 choices.

Q6: The quality of our online course instruction has improved
 significantly since the online program began.

Q7: There is substantial student demand for additional online courses
 at our university.

Q8: Fewer hours of professor labor are required for an online course
 than for the same course taught on-campus.

Q9: Instructors should be paid more for teaching online than for
 teaching on-campus.

Q10: Given the choice I would prefer teaching on-campus to
 teaching online.

Q11: Students learn as much in an online course as they do in the
 same course taught on-campus.

Q12: Students get as much value for their money in an online course
 as they do in an on-campus course.

Q13: It is easy to engage online students in class discussions
 via the internet.

Q14: It is more difficult to meet the needs of online students
 than of on-campus students.

Table 2: Analysis By Online Experience

 Online Std.
 Exper. N Mean Deviation t-statistic p-value

Q #1 yes 39 4.0769 .8998 2.290 0.024
 no 71 3.5493 1.2738
Q #2 yes 39 3.1282 1.5249 -0.433 0.666
 no 71 3.2394 1.1397
Q #3 yes 39 3.8718 1.3412 4.005 0.000
 no 71 2.7606 1.4189
Q #4 yes 39 2.4615 1.1203 -3.039 0.003
 no 71 3.1127 1.0495
Q #5 yes 39 2.6154 .7475 -2.172 0.032
 no 71 3.0000 .9562
Q #6 yes 39 2.3846 1.1382 -1.256 0.212
 no 71 2.6056 .7067
Q #7 yes 39 2.3590 1.1118 -2.292 0.024
 no 71 2.8451 1.0371
Q #8 yes 39 4.1026 .5024 0.336 0.737
 no 71 4.0563 .7725
Q #9 yes 39 3.0000 1.6859 -0.917 0.361
 no 71 3.2817 1.4559
Q #10 yes 39 3.3077 1.7038 4.922 0.000
 no 71 1.9718 1.1335
Q #11 yes 39 2.4615 1.3148 -4.588 0.000
 no 70 3.5714 1.1493

Q #12 yes 39 2.3590 1.4046 -4.137 0.000
 no 71 3.4366 1.2505
Q #13 yes 39 3.3846 1.5151 -0.213 0.832
 no 71 3.4366 1.0383
Q #14 yes 39 2.5897 1.4458 -0.229 0.819
 no 71 2.6479 1.1723

Table 3: Analysis by Gender

 Std.
 Gender N Mean Deviation t-statistic p-value

Q #1 male 57 3.4211 1.2385 -3.200 0.002
 female 51 4.1176 .9929

Q #2 male 57 3.0526 1.2736 -1.221 0.225
 female 51 3.3529 1.2779

Q #3 male 57 2.7368 1.5298 -3.334 0.001
 female 51 3.6471 1.2779

Q #4 male 57 3.0877 1.2142 2.295 0.024
 female 51 2.6078 .9182

Q #5 male 57 2.7193 .8609 -1.849 0.067
 female 51 3.0392 .9372

Q #6 male 57 2.6140 .9591 1.180 0.241
 female 51 2.4118 .8044

Q #7 male 57 2.6667 1.0911 -0.093 0.926
 female 51 2.6863 1.1044

Q #8 male 57 4.1053 .8169 0.645 0.520
 female 51 4.0196 .5095

Q #9 male 57 3.2807 1.5440 0.878 0.382
 female 51 3.0196 1.5426

Q #10 male 57 1.9825 1.3295 -3.565 0.001
 female 51 2.9608 1.5226

Q #11 male 57 3.3684 1.2905 1.539 0.127
 female 50 2.9800 1.3169

Q #12 male 57 3.3158 1.4535 2.003 0.048
 female 51 2.7843 1.2855

Q #13 male 57 3.5614 1.1498 1.148 0.253
 female 51 3.2941 1.2696

Q #14 male 57 2.4737 1.2692 -1.043 0.299
 female 51 2.7255 1.2342

Table 4: Analysis by PC Literacy

 PC Std.
 Literacy N Mean Deviation t-statistic p-value

Q #1 low 11 3.4545 .9342 -0.835 0.406
 high 99 3.7677 1.2023

Q #2 low 11 3.4545 1.3685 0.692 0.491
 high 99 3.1717 1.2781

Q #3 low 11 2.9091 1.6404 -0.576 0.566
 high 99 3.1818 1.4733

Q #4 low 11 2.9091 .9439 0.085 0.932
 high 99 2.8788 1.1363

Q #5 low 11 3.0909 1.0445 0.879 0.382
 high 99 2.8384 .8888

Q #6 low 11 2.8182 .4045 1.151 0.252
 high 99 2.4949 .9189

Q #7 low 11 3.2727 .9045 1.959 0.053
 high 99 2.6061 1.0863

Q #8 low 11 4.4545 .6876 1.969 0.052
 high 99 4.0303 .6769

Q #9 low 11 4.0000 1.0000 1.879 0.063
 high 99 3.0909 1.5655

Q #10 low 11 2.5455 1.5725 0.232 0.817
 high 99 2.4343 1.4994

Q #11 low 11 2.9091 1.221 -0.702 0.484
 high 98 3.2041 1.3313

Q #12 low 11 3.0909 1.3751 0.090 0.928
 high 99 3.0505 1.4097

Q #13 low 11 3.0000 1.0000 -1.199 0.233
 high 99 3.4646 1.2398

Q #14 low 11 3.4545 1.1282 2.323 0.022
 high 99 2.5354 1.2561

Table 5: Analysis by Age

 Std.
 Age N Mean Deviation t-statistic p-value

Q #1 41 up 79 3.7468 1.1262 0.148 0.883
 40 under 31 3.7097 1.3215

Q #2 41 up 79 3.1139 1.3106 -1.124 0.263
 40 under 31 3.4194 1.2048

Q #3 41 up 79 3.2152 1.5079 0.682 0.497
 40 under 31 3.0000 1.4376

Q #4 41 up 79 2.8608 1.1179 -0.315 0.753
 40 under 31 2.9355 1.1236

Q #5 41 up 79 2.8987 .9001 0.649 0.518
 40 under 31 2.7742 .9205

Q #6 41 up 79 2.6203 .8815 1.776 0.079
 40 under 31 2.2903 .8638

Q #7 41 up 79 2.7342 1.0944 0.948 0.345
 40 under 31 2.5161 1.0605

Q #8 41 up 79 4.0886 .6829 0.386 0.701
 40 under 31 4.0323 .7063

Q #9 41 up 79 3.2278 1.4759 0.499 0.619
 40 under 31 3.0645 1.7114

Q #10 41 up 79 2.5570 1.5587 1.248 0.215
 40 under 31 2.1613 1.3190

Q #11 41 up 78 3.1026 1.3444 -0.901 0.370
 40 under 31 3.3548 1.2530

Q #12 41 up 79 3.0000 1.3960 -0.651 0.517
 40 under 31 3.1935 1.4241

Q #13 41 up 79 3.3165 1.2041 -1.400 0.164
 40 under 31 3.6774 1.2487

Q #14 41 up 79 2.6835 1.3062 0.740 0.461
 40 under 31 2.4839 1.1796

Table 6: Analysis by Tenure

 Std.
 Tenure N Mean Deviation t-statistic p-value

Q #1 yes 63 3.5397 1.1334 -2.057 0.042
 no 47 4.0000 1.1978

Q #2 yes 63 3.2381 1.2916 0.359 0.720
 no 47 3.1489 1.2850

Q #3 yes 63 3.0476 1.4857 -0.873 0.384
 no 47 3.2979 1.4878

Q #4 yes 63 2.9524 1.1836 0.767 0.445
 no 47 2.7872 1.0201

Q #5 yes 63 2.8889 .9352 0.338 0.736
 no 47 2.8298 .8678

Q #6 yes 63 2.5556 .9466 0.386 0.700
 no 47 2.4894 .8041

Q #7 yes 63 2.8413 1.0657 1.910 0.059
 no 47 2.4468 1.0796

Q #8 yes 63 3.9841 .7294 -1.577 0.118
 no 47 4.1915 .6128

Q #9 yes 63 3.3810 1.4304 1.581 0.117
 no 47 2.9149 1.6528

Q #10 yes 63 2.3175 1.4682 -1.037 0.302
 no 47 2.6170 1.5401

Q #11 yes 63 3.1270 1.3379 -0.437 0.663
 no 46 3.2391 1.3027

Q #12 yes 63 3.0000 1.4142 -0.471 0.638
 no 47 3.1277 1.3928

Q #13 yes 63 3.2381 1.1875 -1.808 0.073
 no 47 3.6596 1.2385

Q #14 yes 63 2.6508 1.3218 0.224 0.823
 no 47 2.5957 1.2097

Table 7: Summary of Significant Differences of Response Means (p<0.05)

 Online Exp. Gender PC Literacy Age Tenure

Q#1 Yes Yes Yes
Q#2
Q#3 Yes Yes
Q#4 Yes Yes
Q#5 Yes
Q#6
Q#7 Yes Yes
Q#8 Yes
Q#9
Q#10 Yes Yes
Q#11 Yes
Q#12 Yes yes
Q#13
Q#14 Yes